Method, medium, and system for detecting potato virus in a crop image

ABSTRACT

A method of detecting a potato virus in a crop image depicting at least one potato plant includes storing the crop image in a memory, identifying a first region of the crop image depicting potato plant leaves, identifying a plurality of edges within the first region, determining whether an image segment of the crop image within the first region satisfies one or more leaf creasing criteria symptomatic of leaf creasing caused by the virus based on the edges that are located within the image segment, determining whether the image segment satisfies one or more color criteria symptomatic of discoloration caused by the virus, and determining whether the segment displays symptoms of potato virus based on whether the image segment satisfies one or more of the leaf creasing criteria and the color criteria. A system and computer readable medium are also disclosed.

FIELD

The present application relates to methods, mediums, and systems fordetecting potato virus in crop images.

INTRODUCTION

In recent years, potato viruses, such as potato virus Y, have haddevastating effects on potato crops in various parts of the world. Ithas been reported that an infected potato field may ultimately result in10-100% loss in yield. Potato viruses are commonly spread by aphidvectors which acquire viruses from infected plants and spread theviruses to healthy plants they later feed upon. The spread of the viruscan be mitigated by rogueing infected plants. However, searching forinfected plants in large crop fields can be challenging and timeconsuming.

SUMMARY

In one aspect, the disclosure relates to a method of detecting a potatovirus in a crop image depicting at least one potato plant. The methodcomprises storing the crop image in a memory; identifying, by aprocessor, a first region of the crop image, the first region depictingpotato plant leaves, wherein the first region is exclusive of a secondregion of the crop image, the second region depicting non-leaf imagery;identifying, by the processor, a plurality of edges within the firstregion; determining, by the processor, whether an image segment of thecrop image within the first region satisfies one or more leaf creasingcriteria based on the edges that are located within the image segment,wherein the leaf creasing criteria are symptomatic of leaf creasingcaused by the virus; determining, by the processor, whether the imagesegment satisfies one or more color criteria symptomatic ofdiscoloration caused by the virus; and determining, by the processor,whether the segment displays symptoms of potato virus based on whetherthe image segment satisfies one or more of the leaf creasing criteriaand the color criteria.

In another aspect, the disclosure relates to a computer-readable mediumcomprising instructions executable by a processor, wherein theinstructions when executed configure the processor to: store the cropimage in a memory; identify a first region of the crop image, the firstregion depicting potato plant leaves, wherein the first region isexclusive of a second region of the crop image, the second regiondepicting non-leaf imagery; identify a plurality of edges within thefirst region; determine whether an image segment of the crop imagewithin the first region satisfies one or more leaf creasing criteriabased on the edges that are located within the image segment, whereinthe leaf creasing criteria are symptomatic of leaf creasing caused by apotato virus; determine whether the image segment satisfies one or morecolor criteria symptomatic of discoloration caused by the virus; anddetermine whether the segment displays symptoms of potato virus based onwhether the image segment satisfies one or more of the leaf creasingcriteria and the color criteria.

In a further aspect, the disclosure relates to a system for detectingpotato virus in a crop image containing potato plants, the systemcomprising: a memory storing computer readable instructions and the cropimage; and a processor configured to execute the computer readableinstructions, the computer readable instructions configuring theprocessor to: store the crop image in a memory; identify a first regionof the crop image, the first region depicting potato plant leaves,wherein the first region is exclusive of a second region of the cropimage, the second region depicting non-leaf imagery; identify aplurality of edges within the first region; determine whether an imagesegment of the crop image within the first region satisfies one or moreleaf creasing criteria based on the edges that are located within theimage segment, wherein the leaf creasing criteria are symptomatic ofleaf creasing caused by the virus; determine whether the image segmentsatisfies one or more color criteria symptomatic of discoloration causedby the virus; and determine whether the segment displays symptoms ofpotato virus based on whether the image segment satisfies one or more ofthe leaf creasing criteria and the color criteria.

DRAWINGS

FIG. 1 shows a schematic illustration of a system, in accordance with anembodiment;

FIG. 2 is a flowchart illustrating a method of detecting potato virus ina crop image;

FIG. 3 is a flowchart illustrating a method of identifying potato plantleaves in a crop image;

FIG. 4 is an example of a crop image;

FIG. 5 shows the magenta channel of the crop image of FIG. 4;

FIG. 6 is a binary image based on the magenta channel of FIG. 5;

FIG. 7 is the binary image of FIG. 6 after dilation;

FIG. 8 is a magenta channel based image mask based on the dilated imageof FIG. 7;

FIG. 9 the crop image of FIG. 4 after masking non-leaf regions;

FIG. 10 is an image depicting edges detected in the crop image of FIG.4;

FIG. 11 is an image identifying image segments satisfying edge criteria;

FIG. 12 is an image identifying image segments satisfying line criteria;

FIG. 13 is an image identifying image segments satisfying contourcriteria;

FIG. 14 is an image identifying image segments satisfying colorcriteria; and

FIG. 15 is an image identifying image segments satisfying at least oneof edge, line, contour, and color criteria.

DESCRIPTION OF VARIOUS EMBODIMENTS

Numerous embodiments are described in this application, and arepresented for illustrative purposes only. The described embodiments arenot intended to be limiting in any sense. The invention is widelyapplicable to numerous embodiments, as is readily apparent from thedisclosure herein. Those skilled in the art will recognize that thepresent invention may be practiced with modification and alterationwithout departing from the teachings disclosed herein. Althoughparticular features of the present invention may be described withreference to one or more particular embodiments or figures, it should beunderstood that such features are not limited to usage in the one ormore particular embodiments or figures with reference to which they aredescribed.

The terms “an embodiment,” “embodiment,” “embodiments,” “theembodiment,” “the embodiments,” “one or more embodiments,” “someembodiments,” and “one embodiment” mean “one or more (but not all)embodiments of the present invention(s),” unless expressly specifiedotherwise.

The terms “including,” “comprising” and variations thereof mean“including but not limited to,” unless expressly specified otherwise. Alisting of items does not imply that any or all of the items aremutually exclusive, unless expressly specified otherwise. The terms “a,”“an” and “the” mean “one or more,” unless expressly specified otherwise.

Although method steps may be described or listed in the disclosure andin the claims in a sequential order, such methods may be configured towork in alternate orders. In other words, any sequence or order of stepsthat may be described does not necessarily indicate a requirement thatthe steps be performed in that order. The steps of methods describedherein may be performed in any order that is practical. Further, somesteps may be performed simultaneously, and some steps may be omitted.

Known methods for detecting for potato virus include sending physicalplant samples to laboratories for testing. The time to collect and shipsamples and wait for results can create delay that leads to furtherspreading of the virus. Also, for large crop fields, it may beimpractical to collect, ship, and pay for testing enough samples toreliably detect potato virus across the whole plantation.

Embodiments disclosed herein relate to image based detection of potatovirus. This may provide a fast, accurate, and inexpensive alternative tolaboratory based testing of potato crops for potato virus. In anembodiment of the disclosure, the potato virus is a potato mosaic virus.In various embodiments of the disclosure, the potato virus is a potatovirus X (PVX), potato virus S (PVS), potato virus M (PVM), potato virusY (PVY), or potato virus A (PVA), or a combination of two or more suchviruses. By way of overview, crop images of a crop field (including, forexample, a crop field in a greenhouse) containing plants, for example,potato plants, are captured for analysis. For example, aerial drones orcameras mounted to farm equipment (e.g. a combine harvester) can be usedto capture crop images. A computer processor manipulates and analyzesthe crop images for visible symptoms of potato virus, such as leafcreasing and leaf discoloration. Based on the severity of the detectedsymptoms, the processor identifies whether the crop image containsinfected plants, for example, infected potato plants. With thisinformation, the identified plants, for example, the identified potatoplants can be rogued to mitigate the spread of the virus. A potato virusof the disclosure may infect a plant, for example, a plant of the familySolanaceae, such as a potato plant. Thus, in various embodiments, themethod, computer-readable medium, and system of the disclosure relate tothe detection of a potato virus in a crop image depicting a plant, forexample, a plant of the family Solanaceae, such as a potato plant. Invarious embodiments, a potato plant of the disclosure is any potatoplant (Solanum tuberosum L.), for example, waxy potato (e.g. fingerlingpotatoes), starchy potato (e.g. Russet Burbank), yellow potato (e.g.Yukon gold potato), white potato (e.g. Shepody), red potato, bluepotato, or a combination of two or more such plants.

FIG. 1 shows an example schematic of a system 100. Generally, a system100 can be a server computer, desktop computer, notebook computer,tablet, PDA, smartphone, or another system that can perform the methodsdescribed herein. In at least one embodiment, system 100 includes aconnection with a network 116 such as a wired or wireless connection tothe Internet or to a private network.

In the example shown, system 100 includes a memory 102, an application104, an output device 106, a display device 108, a secondary storagedevice 110, a processor 112, and an input device 114. In someembodiments, system 100 includes multiple of any one or more of memory102, application 104, output device 106, display device 108, secondarystorage device 110, processor 112, input device 114, and networkconnections (i.e. connections to network 116 or another network). Insome embodiments, system 100 does not include one or more ofapplications 104, secondary storage devices 110, network connections,input devices 114, output devices 106, and display devices 108.

Memory 102 can include random access memory (RAM) or similar types ofmemory. Also, in some embodiments, memory 102 stores one or moreapplications 104 for execution by processor 112. Application 104corresponds with software modules including computer executableinstructions to perform processing for the functions and methodsdescribed below. Secondary storage device 110 can include a hard diskdrive, floppy disk drive, CD drive, DVD drive, Blu-ray drive, solidstate drive, flash memory or other types of non-volatile data storage.

In some embodiments, system 100 stores information in a remote storagedevice, such as cloud storage, accessible across a network, such asnetwork 116 or another network. In some embodiments, system 100 storesinformation distributed across multiple storage devices, such as memory102 and secondary storage device 110 (i.e. each of the multiple storagedevices stores a portion of the information and collectively themultiple storage devices store all of the information). Accordingly,storing data on a storage device as used herein and in the claims meansstoring that data in a local storage device; storing that data in aremote storage device; or storing that data distributed across multiplestorage devices, each of which can be local or remote.

Generally, processor 112 can execute applications, computer readableinstructions, or programs. The applications, computer readableinstructions, or programs can be stored in memory 102 or in secondarystorage 110, or can be received from remote storage accessible throughnetwork 116, for example. When executed, the applications, computerreadable instructions, or programs can configure the processor 112 (ormultiple processors 112, collectively) to perform one or more acts ofthe methods described herein, for example.

Input device 114 can include any device for entering information intodevice 100. For example, input device 114 can be a keyboard, key pad,cursor-control device, touch-screen, camera, or microphone. Input device114 can also include input ports and wireless radios (e.g. Bluetooth® or802.11x) for making wired and wireless connections to external devices.

Display device 108 can include any type of device for presenting visualinformation. For example, display device 108 can be a computer monitor,a flat-screen display, a projector, or a display panel.

Output device 106 can include any type of device for presenting a hardcopy of information, such as a printer for example. Output device 106can also include other types of output devices such as speakers, forexample. In at least one embodiment, output device 106 includes one ormore of output ports and wireless radios (e.g. Bluetooth® or 802.11x)for making wired and wireless connections to external devices.

FIG. 1 illustrates one example hardware schematic of a system 100. Inalternative embodiments, system 100 contains fewer, additional, ordifferent components. In addition, although aspects of an implementationof system 100 are described as being stored in memory, one skilled inthe art will appreciate that these aspects can also be stored on or readfrom other types of computer program products or computer-readablemedia, such as secondary storage devices, including hard disks, floppydisks, CDs, or DVDs; or other forms of RAM or ROM.

FIG. 1 is to be referred to for the remainder of the descriptionwherever reference is made to system 100 or a component thereof.

The flowcharts in the Figures illustrate the architecture,functionality, and operation of possible implementations of systems,methods and computer readable media according to various embodiments. Inthis regard, each block in the flowcharts may represent a module,segment, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). It willbe appreciated that any one or more (or all) blocks of the flowchartscan be implemented by special purpose hardware-based systems thatperform the specified functions or acts, or by combinations of specialpurpose hardware and computer instructions.

Reference is now made to FIG. 2, which shows a flowchart illustrating amethod 200 of detecting potato virus in a crop image. At 204, a cropimage is stored in memory 102. An example of a crop image 400 is shownin FIG. 4. As shown, crop image 400 may be a photograph taken from abovea crop field, looking downwardly towards the potato plants 404. Theaerial perspective can provide good visibility of the potato plantleaves 408, which display the virus symptoms that the method relies uponfor its analysis.

The crop image 400 can be taken in any manner, with any camera orcamera-equipped device. For example, the crop image 400 can be taken bya farmer or service provider using a digital camera (e.g.point-and-shoot, digital SLR, or video camera), a camera-equippedsmartphone, a camera mounted to farm equipment (e.g. a combineharvester), or a camera-equipped drone. The crop image 400 can include adiscrete photograph, an image stitched together from many photographs(e.g. panorama), or one or more frames of a video recording, forexample.

The crop image 400 can include any number of potato plants. For example,the crop image 400 can include between a portion of one potato plant andan entire crop-field of potato plants. Preferably, crop image 400includes a plurality of potato plants. This can allow the detectionmethod to perform a computationally efficient bulk analysis on theplurality of potato plants shown in a crop image. For example, a cropfield of several hundred acres may be captured by a few hundredphotographs or less (e.g. 1-700 photographs), which can allow forefficient analysis by the method 200. The computational efficiency ofthe method 200 can allow an entire crop field to be analyzed for potatovirus on a regular basis (e.g. daily, weekly, or monthly).

The method 200 determines whether a crop image contains potato virusbased on visible symptoms which appear on the leaves of the depictedpotato plants. At 208, processor 112 identifies a first region of cropimage 400 (FIG. 4) containing potato leaves, which is exclusive of asecond region of the crop image 400 (FIG. 4) containing non-leafimagery, such as dirt and debris. In some embodiments, processor 112 maydelete, paint over, or otherwise alter the second region to exclude thatsecond region from subsequent analysis. For example, processor 112 maycreate and apply one or more image masks to crop image 400 (FIG. 4) inorder to remove non-leaf imagery from subsequent analysis.

FIG. 3 is a flowchart illustrating a method 300 of identifying potatoleaves in a crop image, which includes creating and applying twocolor-based image masks to the crop image. Steps 304-316 relate to thecreation of a magenta plane based image mask, and step 320 relates tothe creation of an RGB based color mask. The two masks are applied tothe crop image 400 (FIG. 4) at 324. It will be appreciated that althoughgood results have been obtained by creating and applying both of thedescribed color-based image masks, satisfactory results may be achievedby creating and applying just one of the two color-based image masks, orone or more different color-based image masks. In some embodiments,identifying the potato leaves may include creating and applying one orboth of the described color-based image masks, in addition to creatingand applying another color-based image mask.

Most cameras are configured to capture images mapped to RGB space. At304, processor 112 creates a CMYK image from the crop image 400 (FIG. 4)and stores the image in memory 102. Processor 112 can convert the cropimage (or a copy thereof) to a CMYK image according to any method knownin the art. This step can be omitted where the captured crop image 400(FIG. 4) is already mapped to the CMYK color space.

The inventors have found that the magenta plane of a crop image iseffective for isolating non-leaf imagery. At 308, processor 112 createsa binary image from the magenta plane of the CMYK image. FIG. 5 shows anexample of the magenta plane 500 of crop image 400 (FIG. 4). FIG. 6shows an example of a binary image 600 created based on the magentaplane 500 (FIG. 5) of crop image 400 (FIG. 4). In a binary image, all ofthe pixels are either a first or second color (typically white orblack). For clarity of illustration, the examples below refer to binaryimages as having white or black pixels. However, it is expresslycontemplated that in other embodiments, a binary image can be formed byany two colors.

The magenta plane 500 may be binarized by setting each pixel to black orwhite based on whether the pixel satisfies one or more magenta criteria.The magenta criteria may include a threshold minimum or maximum magentavalue, one or more magenta value ranges, or combinations thereof. Pixelsthat have magenta values above or below the threshold magenta value,and/or that have magenta values within or outside of one or more of themagenta value ranges will all be set to white or all be set to black.The magenta criteria may be predetermined for application to a pluralityof crop images, or determined separately for each crop image. Forexample, the crop image 400 (FIG. 4) may undergo pre-processing tocorrect for image characteristics, such as white balance and lightingconditions, to provide sufficient uniformity to apply pre-determinedmagenta criteria. In other embodiments, magenta criteria are determinedfor each crop image 400 (FIG. 4) based on image characteristics (e.g.lighting and white balance) of the particular crop image. The binaryimage 600 of FIG. 6 was prepared with magenta criteria including athreshold magenta value of 0 on a scale from 0 to 255, where pixelshaving a magenta value above the threshold magenta value were set towhite and where white pixels represent non-leaf imagery 604.

It will be appreciated that a mathematical relationship exists for thepixel-wise conversion of an RGB image to a CMYK image, so that analgorithm can be devised to create magenta-based binary image 600 froman RGB crop image without having to create or store a CMYK image.

At 312, processor 112 morphologically dilates binarized image 600 (FIG.6) to create a dilated binarized image 700 (FIG. 7) having an enlargednon-leaf region 704 (e.g. white pixel region). This can be helpful forcapturing additional non-leaf imagery from the crop image, especiallywhere a conservative magenta profile was applied at 308 to avoidcapturing plant leaves in the non-leaf region 604 (FIG. 6). For example,the magenta profile applied at 308 may not consistently capture portionsof the non-leaf region which border plant leaves, and the morphologicaldilation may be effective at expanding the non-leaf region 704 (FIG. 7)to capture these border portions. In alternative embodiments, themagenta profile applied at 308 may be sufficiently accurate, so that themorphological dilation at 312 can be omitted.

At 316, the processor 112 creates a first mask from the dilated binaryimage 700 (FIG. 7). Referring to FIG. 8, processor 112 may invert binaryimage 700 (FIG. 7) to create image mask 800. In the illustrated example,this allows non-leaf region 804 to be represented by black pixels, andthe leaf region 808 to be represented by white pixels. This conforms toindustry standards wherein black pixels in an image mask delete from (orpaint-over) the image to which they are applied. For example, when imagemask 800 is applied to crop image 400 (FIG. 4), the black non-leafregion 804 of image mask 800 will paint over the corresponding portionof crop image 400 (FIG. 4) with black, and the white leaf-region 808 ofimage mask 800 will leave the corresponding portion of crop image 400(FIG. 4) undisturbed.

In alternative embodiments, the binarized image 600 (FIG. 6) created at308 or dilated binarized image 700 (FIG. 7) created at 312 may be useddirectly as a mask without color inversion, by configuring the maskingoperation to treat the white and black pixels oppositely to standardconvention.

At 320, processor 112 creates a second mask based on color channelthresholding (e.g. RGB thresholding). For example, processor 112 maycreate an image mask by binarizing crop image 400 (FIG. 4) based oncolor channel criteria (e.g. RGB criteria). The color channel criteriamay include one or more predetermined threshold color channel values(e.g. RGB values), one or more predetermined color channel value ranges(e.g. RGB value ranges), or combinations thereof. For example, pixelsthat have RGB values above or below the threshold RGB values or thathave RGB values within or outside one or more of the RGB value rangeswill all be set to white or all be set to black. In one example, the RGBcriteria includes an RGB value range of (17, 54, 17) to (174, 211, 153),where each of the Red, Green, and Blue channels are mapped within arange of 0-255, where pixels within the RGB value range are set to blackto represent the non-leaf region and where the remaining pixels are setto white to represent the leaf region.

At 324, processor 112 applies the created color-based mask(s) to thecrop image 400 (FIG. 4) to create a masked crop image. FIG. 9 shows anexemplary masked crop image 900 created by masking crop image 400 (FIG.4) with the magenta plane based mask created at 316 and further maskedby the RGB based mask created at 320. As shown, the painted-over secondregion 904 contains few or no plant leaves and the remaining firstregion 908 contains predominantly plants leaves with little or nonon-leaf imagery. For example, first region 908 includes at least 80% ofthe plant leaves depicted in crop image 400 (FIG. 4), and second region904 includes at least 80% of the non-leaf imagery depicted in crop image400 (FIG. 4). During subsequent processing, leaf creasing and leafdiscoloration are assessed based on the remaining first region 908.

Reference is now made to FIG. 2. After identifying first region 908(FIG. 9) containing potato plant leaves 408 of crop image 400 (FIG.4),the method proceeds with assessing the first region 908 (FIG. 9) forsymptoms of potato virus and weighing those symptoms to determinewhether the potato plants in the crop image are infected with potatovirus (FIG. 9).

At 212, processor 112 segments crop image 400 (FIG. 4) into imagesegments. For example, processor 112 may conceptually divide crop image400 (FIG. 4) or at least first region 908 (FIG. 9) into an array ofdistinct image segments. Each image segment can represent a distinctanalytical block. Processor 112 may separately assess each image segmentfor virus symptoms. For example, processor 112 may repeat each of steps216 to 236 for each image segment. Processor 112 may then determinewhether any potato plants in the crop image 400 (FIG. 4) are infectedwith potato virus based on the quantum and grouping of image segmentsdisplaying virus symptoms.

Processor 112 can segment crop image 400 into any number of imagesegments (e.g. greater than 10 segments, such as 10-10,000 segments).The number of image segments may depend on the resolution and field ofview of the crop image 400. For example, where the field of view of cropimage 400 is small (e.g. crop image 400 captures very few plants or onlya portion of a plant), then processor 112 may segment crop image 400into relatively few image segments (e.g. 10-50 segments) so thatindividual image segments include a sufficient portion of a potato plantwith which to perform an analysis for virus symptoms. In contrast, wherethe field of view of crop image 400 is large (e.g. crop image 400captures many plants), then processor 112 may segment crop image 400into many image segments (e.g. 51-10,000 segments) so that each plant orleaf in the crop image 400 is divided among several image segments foranalysis. An image segment can have any size and shape. FIGS. 11-15 showexamples including image segments 1104, 1204, 1304, 1404, and 1505 thatare rectangular and uniformly sized. This may simplify the division ofthe image into image segments. In other embodiments, processor 112 maysegment crop image 400 into image segments that are non-rectangular,such as circular or triangular segments, or segments of other regular orirregular shapes. Moreover, in some embodiments, processor 112 maysegment crop image 400 into image segments of non-uniform shape and/orsize. For example, the segments may include segments of multipledifferent shapes and/or multiple different sizes.

One symptom of some potato viruses, such as potato virus Y, is leafcreasing. At 216, processor 112 identifies leaf creasing within firstregion 908 (FIG. 9). Processor 112 can apply any process or algorithmthat is effective for identifying leaf creasing. As compared withconventional texture analysis methods (e.g. GLCM texture analysis), theinventors have found that leaf creasing can be more quickly andcomputationally efficiently identified through the use of one or more(or all) of edge, line, and contour detection methods. In general,greater edges and lines and smaller contour areas within a segment offirst region 908 (FIG. 9) can be symptomatic of potato virus.

At 220, processor 112 detects edges within the image segments of firstregion 908 (FIG. 9) and compares the detected edges against edgecriteria symptomatic of potato virus leaf creasing. Processor 112 mayuse any edge detection method suitable for detecting edges within plantleaves, such as for example Canny edge detection. Canny edge detectionuses dual (upper and lower) pixel gradient thresholds to distinguishdetected edges from noise or natural color variation. For example, theupper and lower thresholds for Canny edge detection may be provided asfollows:

${{upper}\mspace{14mu} {threshold}} = \left( \frac{1 + {sigma}}{mean} \right)$${{lower}\mspace{14mu} {threshold}} = \left( \frac{1 - {sigma}}{mean} \right)$

In operation, if a pixel gradient value is greater than the upperthreshold, the pixel is accepted as an edge; if a pixel gradient valueis below the lower threshold, then it is rejected; and if a pixelgradient value is between the two thresholds, then it will be acceptedas an edge only if it is connected to a pixel that is above the upperthreshold. In this example, the upper threshold is one standarddeviation above the average gradient in the image segment, and the lowerthreshold is one standard deviation below the average gradient in theimage segment.

FIG. 10 is an image 1000 including edges 1004 detected by processor 112within first region 908 (FIG. 9) represented by white pixels. Processor112 may compare the detected edges 1004 (FIG. 10) against edge criteriasymptomatic of potato virus leaf creasing. The edge criteria may includea threshold minimum quantum of edges, such as a threshold minimum numberof edge pixels (e.g. white pixels) as an absolute number or as aproportion of the number of pixels within the segment (e.g. greater than10% edge pixels). FIG. 11 illustrates an exemplary image 1100 showingsegments 1104 identified by processor 112 as having greater than 13.8%edge pixels as being symptomatic of potato virus.

At 224, processor 112 detects discrete lines within the first region 908(FIG. 9) defined by the edges 1004 (FIG. 10) detected at 220, andcompares the detected lines against line criteria symptomatic of potatovirus. Processor 112 may use any line detection method suitable fordetecting lines within the edges detected at 220, such as Hough linedetection for example. The line criteria may include a threshold minimumquantum of lines, such as a threshold minimum number of lines having athreshold minimum length. The threshold number of lines may be expressedas an absolute number or a density of real world area depicted by thesegment (e.g. lines per square centimeter). The threshold length may beexpressed as an absolute number of pixels, a real-world measurement(e.g. centimeters), or as a proportion of a dimension of the segment(e.g. percentage of the segment width), for example. FIG. 12 illustratesan exemplary image 1200 showing image segments 1204 identified byprocessor 112 as having at least 50 lines with a length of at least 50pixels (e.g. at least 30% of the segment width).

At 228, processor 112 detects contours within the first region 908 (FIG.9) defined by edges 1004 (FIG. 10) detected at 220, and compares thedetected contours against contour criteria symptomatic of potato virusleaf creasing. A contour is a closed shape formed by the edges 1004(FIG. 10) detected at 220 (e.g. a region completely surrounded by edgepixels). The inventors have found that, if no contour within a segmenthas an area exceeding a specific threshold area (e.g. 1500 pixels), thensuch segment is more likely to exhibit creasing symptomatic of potatovirus. The contour criteria may include a threshold maximum contourarea, which may be expressed as an absolute number of pixels, areal-world measurement (e.g. square centimeters), or as a proportion ofthe segment area (e.g. percentage of segment area). FIG. 13 illustratesan exemplary image 1300 showing image segments 1304 identified byprocessor 112 which meet a contour criterion, which is the absence ofcontours having an individual contour area exceeding 1500 pixels (e.g.8.5% of the segment area).

Another symptom of some potato viruses, such as potato virus Y, is leafdiscoloration. At 232, processor 112 compares the color profile of eachimage segment within first region 908 (FIG. 9) against color criteria.The color profile of an image segment can include any one or more valuesof any color property of that segment, which may include any one or morehistogram properties (e.g. mean, mode, sigma, full width at halfmaximum, root mean squared, percentile, minimum, and maximum) for anychannel or channels of any one or more color spaces (e.g., withoutlimitation, RGB, CMYK, HSV, and HSL). Similarly, the color criteria caninclude any one or more values and/or value ranges of any such colorproperty, where those values or value ranges may be symptomatic ofpotato virus leaf discoloration.

In one embodiment, the color profile of a segment includes the Euclidiandistance in a color cone between two average color channel values inthat segment. For example, the color profile may include the Euclidiandistance in a color cone between the average green and red values, andbetween the average green and blue color values in the segment. FIG. 14illustrates an exemplary image 1400 showing image segments 1404 that theprocessor 112 has identified as satisfying the following color criteria:Euclidean distance between average green and red of less than 3 orgreater than 45.5, and Euclidean distance between average green and blueof less than 10 or greater than 113. Segments 1404 are symptomatic ofpotato virus discoloration.

At 236, processor 112 determines whether each segment displays symptomsof potato virus based on the leaf creasing criteria assessed at 216-228and the color criteria assessed at 232. In some embodiments, processor112 may assign a weighted value to the result of each leaf creasing andcolor comparison, and determine that a segment displays symptoms ofpotato virus where the sum of those weighted values exceeds apredetermined threshold. For example, processor 112 may assign a valueof 20% for satisfying the edge criteria at 220, a value of 20% forsatisfying the line criteria at 224, a value of 20% for satisfying thecontour criteria at 228, and a value of 40% for satisfying the colorcriteria at 232, and then determine that a segment displays symptoms ofpotato virus where the sum exceeds 50%. This example allows a segment tobe identified as displaying symptoms of potato virus where all of theleaf creasing criteria are satisfied, or where the color criteria and atleast one leaf creasing criteria are satisfied. FIG. 15 illustrates anexemplary image 1500 showing image segments 1504 that processor 112 hasidentified as having satisfied at least one criteria (edge, line,contour, or color). Processor 112 has determined a weighted value foreach segment 1504. The segments 1504 having a weighted value exceeding apredetermined threshold (e.g. 50%) are identified by the processor 112as displaying symptoms of potato virus.

At 240, processor 112 determines whether crop image 400 (FIG. 4)contains potato virus based on whether the segments 1504 (FIG. 15)identified as displaying symptoms of potato virus at 236 satisfy quantumcriteria. In some embodiments, the quantum criteria may include athreshold minimum number of segments 1504 (FIG. 15), which may beexpressed as an absolute number (e.g. 5 segments) or a proportion of thetotal number of segments in crop image 400 (FIG. 4) formed at 212 (e.g.0.5% of the total crop image segments). A farmer can use the crop imagesidentified at 240 to locate virus infected plants on their farm (e.g.using the geo-tag or other location information associated with the cropimage) and rogue those plants to prevent further spreading of the virus.This can reduce the crop yield loss due to the potato virus.

While the above description provides examples of the embodiments, itwill be appreciated that some features and/or functions of the describedembodiments are susceptible to modification without departing from thespirit and principles of operation of the described embodiments.Accordingly, what has been described above has been intended to beillustrative of the invention and non-limiting and it will be understoodby persons skilled in the art that other variants and modifications maybe made without departing from the scope of the invention as defined inthe claims appended hereto. The scope of the claims should not belimited by the preferred embodiments and examples, but should be giventhe broadest interpretation consistent with the description as a whole.

1. A method of detecting a potato virus in a crop image depicting atleast one potato plant, the method comprising: storing the crop image ina memory; identifying, by a processor, a first region of the crop image,the first region depicting potato plant leaves, wherein the first regionis exclusive of a second region of the crop image, the second regiondepicting non-leaf imagery; identifying, by the processor, a pluralityof edges within the first region; determining, by the processor, whetheran image segment of the crop image within the first region satisfies oneor more leaf creasing criteria based on the edges that are locatedwithin the image segment, wherein the leaf creasing criteria aresymptomatic of leaf creasing caused by the virus; determining, by theprocessor, whether the image segment satisfies one or more colorcriteria symptomatic of discoloration caused by the virus; anddetermining, by the processor, whether the segment displays symptoms ofpotato virus based on whether the image segment satisfies one or more ofthe leaf creasing criteria and the color criteria.
 2. The method ofclaim 1, further comprising: determining, by the processor, whether thecrop image contains potato virus based on a quantum of image segmentswithin the crop image that are identified as displaying symptoms ofpotato virus.
 3. The method of claim 1, wherein: the one or more leafcreasing criteria include a threshold minimum quantum of edges withinthe image segment.
 4. The method of claim 1, wherein: the one or moreleaf creasing criteria include one or more line criteria, anddetermining whether the image segment satisfies the line criteriacomprises identifying lines within the segment defined by the edges. 5.The method of claim 4, wherein: the one or more line criteria include athreshold minimum quantum of lines.
 6. The method of claim 1, wherein:the one or more leaf creasing criteria include one or more contourcriteria, and determining whether the image segment satisfies thecontour criteria comprises identifying contours within the image segmentdefined by the edges.
 7. The method of claim 6, wherein: the one or morecontour criteria include whether every contour within the segment has anarea not exceeding a threshold maximum area.
 8. The method of claim 1,wherein: the one or more color criteria include one or more value rangesof Euclidian distances in a color cone between two average color channelvalues.
 9. The method of claim 1, wherein: identifying the first regioncomprises creating a first mask based on a magenta channel of the cropimage or of an image generated from the crop image.
 10. The method ofclaim 9, wherein: identifying the first region further comprisescreating a second mask based on one or more predetermined thresholdcolor channel value ranges.
 11. A computer-readable medium comprisinginstructions executable by a processor, wherein the instructions whenexecuted configure the processor to: store the crop image in a memory;identify a first region of the crop image, the first region depictingpotato plant leaves, wherein the first region is exclusive of a secondregion of the crop image, the second region depicting non-leaf imagery;identify a plurality of edges within the first region; determine whetheran image segment of the crop image within the first region satisfies oneor more leaf creasing criteria based on the edges that are locatedwithin the image segment, wherein the leaf creasing criteria aresymptomatic of leaf creasing caused by a potato virus; determine whetherthe image segment satisfies one or more color criteria symptomatic ofdiscoloration caused by the virus; and determine whether the segmentdisplays symptoms of potato virus based on whether the image segmentsatisfies one or more of the leaf creasing criteria and the colorcriteria.
 12. The computer-readable medium of claim 11, wherein theinstructions when executed further configure the processor to: determinewhether the crop image contains potato virus based on a quantum of imagesegments within the crop image that are identified as displayingsymptoms of potato virus.
 13. The computer-readable medium of claim 11,wherein: the one or more leaf creasing criteria include a thresholdminimum quantum of edges within the image segment.
 14. Thecomputer-readable medium of claim 11, wherein: the one or more leafcreasing criteria include one or more line criteria, and determiningwhether the image segment satisfies the line criteria comprisesidentifying lines within the segment defined by the edges.
 15. Thecomputer-readable medium of claim 14, wherein: the one or more linecriteria include a threshold minimum quantum of lines.
 16. Thecomputer-readable medium of claim 11, wherein: the one or more leafcreasing criteria include one or more contour criteria, and determiningwhether the image segment satisfies the contour criteria comprisesidentifying contours within the image segment defined by the edges. 17.The computer-readable medium of claim 16, wherein: the one or morecontour criteria include whether every contour within the segment has anarea not exceeding a threshold maximum area.
 18. The computer-readablemedium of claim 11, wherein: the one or more color criteria include oneor more value ranges of Euclidian distances in a color cone between twoaverage color channel values.
 19. The computer-readable medium of claim11, wherein: identifying the first region comprises creating a firstmask based on a magenta channel of the crop image or of an imagegenerated from the crop image.
 20. The computer-readable medium of claim19, wherein: identifying the first region further comprises creating asecond mask based on one or more predetermined threshold color channelvalue ranges.
 21. A system for detecting potato virus in a crop imagecontaining potato plants, the system comprising: a memory storingcomputer readable instructions and the crop image; and a processorconfigured to execute the computer readable instructions, the computerreadable instructions configuring the processor to: identify a firstregion of the crop image, the first region depicting potato plantleaves, wherein the first region is exclusive of a second region of thecrop image, the second region depicting non-leaf imagery; identify aplurality of edges within the first region; determine whether an imagesegment of the crop image within the first region satisfies one or moreleaf creasing criteria based on the edges that are located within theimage segment, wherein the leaf creasing criteria are symptomatic ofleaf creasing caused by the virus; determine whether the image segmentsatisfies one or more color criteria symptomatic of discoloration causedby the virus; and determine whether the segment displays symptoms ofpotato virus based on whether the image segment satisfies one or more ofthe leaf creasing criteria and the color criteria.
 22. The system ofclaim 21, wherein the computer readable instructions further configurethe processor to: determine whether the crop image contains potato virusbased on a quantum of image segments within the crop image that areidentified as displaying symptoms of potato virus.
 23. The system ofclaim 21, wherein: the one or more leaf creasing criteria include athreshold minimum quantum of edges within the image segment.
 24. Thesystem of claim 21, wherein: the one or more leaf creasing criteriainclude one or more line criteria, and determining whether the imagesegment satisfies the line criteria comprises identifying lines withinthe segment defined by the edges.
 25. The system of claim 24, wherein:the one or more line criteria include a threshold minimum quantum oflines.
 26. The system of claim 21, wherein: the one or more leafcreasing criteria include one or more contour criteria, and determiningwhether the image segment satisfies the contour criteria comprisesidentifying contours within the image segment defined by the edges. 27.The system of claim 26, wherein: the one or more contour criteriainclude whether every contour within the segment has an area notexceeding a threshold maximum area.
 28. The system of claim 21, wherein:the one or more color criteria include one or more value ranges ofEuclidian distances in a color cone between two average color channelvalues.
 29. The system of claim 21, wherein: identifying the firstregion comprises creating a first mask based on a magenta channel of thecrop image or of an image generated from the crop image.
 30. The systemof claim 29, wherein: identifying the first region further comprisescreating a second mask based on one or more predetermined thresholdcolor channel value ranges.